{"as_of":"2026-08-08T06:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f819ec49bcc2bc187f0455a36189033536b7a319539d1b0ba2f24a5e46e60638","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T10:23:12.711159Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T18:28:21.397687Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1705.02755","last_updated":"2017-05-08T06:14:53Z","snapshot_observed_at":"2026-07-06T05:41:39.017376Z","submitted_at":"2017-05-08T06:14:53Z","title":"Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.02755","snapshot_observed_at":"2026-08-06T10:23:12.711159Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.00141","last_updated":"2025-07-31T20:00:35Z","snapshot_observed_at":"2026-08-07T02:39:59.289388Z","submitted_at":"2025-07-31T20:00:35Z","title":"INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T10:23:12.711159Z"},"links":{"cited_paper":"/paper/1705.02755","citing_paper":"/paper/2508.00141"},"observation_digest":"sha256:43248366cfee2155396193fee1e5e29a8205fbb44dc4c36f7093aebb83427e3a","observation_id":"928fe4c9-bbe9-4da4-8809-1c6e05102ee8","resolution":{"observed_at":"2026-08-06T10:23:12.711159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.02755","last_updated":"2017-05-08T06:14:53Z","snapshot_observed_at":"2026-07-06T05:41:39.017376Z","submitted_at":"2017-05-08T06:14:53Z","title":"Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network","version":1},"cited_work":{"arxiv_id":"1705.02755","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.02755","snapshot_observed_at":"2026-08-05T18:28:21.397687Z","title":"Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network","venue":"cs.NI","work_id":"ae4b3a7f-5e59-4626-9e15-236469f6b95a","year":2017},"citing_paper":{"arxiv_id":"2508.14654","last_updated":"2025-08-20T12:13:03Z","snapshot_observed_at":"2026-08-07T21:44:16.818044Z","submitted_at":"2025-08-20T12:13:03Z","title":"Entropy-Constrained Strategy Optimization in Urban Floods: A Multi-Agent Framework with LLM and Knowledge Graph Integration","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T18:28:18.672598Z"},"links":{"cited_paper":"/paper/1705.02755","citing_paper":"/paper/2508.14654"},"observation_digest":"sha256:d112c0518e44656ecace2812d6e7a5970f0ec79462b657747279b53cab6cd09f","observation_id":"36c35b13-e78d-4434-a561-3fe55b359337","resolution":{"observed_at":"2026-08-05T18:28:21.427335Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1705.02755/citation-record","integrity":"/paper/1705.02755/integrity","json":"/paper/1705.02755/citation-record.json","paper":"/paper/1705.02755"},"outbound":[],"paper":{"arxiv_id":"1705.02755","last_updated":"2017-05-08T06:14:53Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-07-06T05:41:39.017376Z","submitted_at":"2017-05-08T06:14:53Z","title":"Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1705.02755."}